Classification of Spatial Data Based on K-means and Vorono¨ı Diagram

This paper is focusing on the problem of the time taken by different algorithms to search data in a large database. The execution time of these algorithms becomes high, in the case of searching data in a non-redundant data, distributed in different database sites where the research consists of reading on each site for finding data. The main purpose is to establish adapted models to represent data in order to facilitate data research. This paper describes a classification of spatial data using a combination of k-means algorithm and vorono¨ı diagram to determine different clusters, representing different group of database sites. The advantages of classification is made through the k-means algorithm that defines the best number and the centers of required clusters and voronoı¨ diagram which gives definitely the delineation of the area with margins, representing the model of organizing data. A composition of K-mean algorithm followed by voronoı¨ diagram has been implemented on simulation data in order to get the clusters, where future parallel research can be realized on different cluster to improve the execution time. In application to e-health in GIS, a best distribution of medical center and available services, will contribute strongly to facilitate population well-being.

@article{KABORE2024,
  title     = {Classification of Spatial Data Based on K-means and Vorono¨ı Diagram},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Moubaric KABORE and Béné-wendé Odilon Isaïe ZOUNGRANA and Abdoulaye SERE},
  doi       = {10.14569/IJACSA.2024.01507139},
  url       = {https://doi.org/10.14569/IJACSA.2024.01507139}
}